Sequence Generalization

Sequence Generalization is a theoretical construct in sequence learning that refers to the ability of a model or system to apply learned sequential patterns to novel, unseen sequences by capturing underlying structural regularities rather than memorizing specific instances.

63

Publications

3.2K

Citations

152

Authors

83

Institutions

Publications per year

2017–2026

29

Authors

152

  1. MB
    Marco Baroni

    University of Trento, Italy

    6 publications · 327 citations

    6

    h-index

  2. BM
    Brenden M. Lake

    New York University, United States

    5 publications · 290 citations

    5

    h-index

  3. AF
    Afra Feyza Akyürek

    Massachusetts Institute of Technology, United States

    2 publications · 46 citations

    2

    h-index

  4. JA
    Jacob Andreas

    University of California, Berkeley, United States

    2 publications · 46 citations

    2

    h-index

  5. JL
    João Loula

    New York University, United States

    2 publications · 117 citations

    2

    h-index

1–5 of 152

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 283 citations

    10

    h-index

  2. ˜The œFibonacci quarterly

    4 publications · 419 citations

    4

    h-index

  3. Theoretical Computer Science

    2 publications · 90 citations

    2

    h-index

  4. Journal of Experimental Psychology General

    2 publications · 132 citations

    2

    h-index

  5. Journal of Experimental Psychology

    2 publications · 60 citations

    2

    h-index

1–5